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* Migrate gsutil usage to gcloud storage * changes for 4312 * Apply automated linter fixes * remove model_garden folder changes * remove model_garden folder changes * Update model_garden_pytorch_llama2_peft_hyperparameter_tuning.ipynb --------- Co-authored-by: gurusai-voleti <gvoleti@google.com>
Get started with BigQuery datasets
Learn how to use `BigQuery` as a dataset for training with `Vertex AI`.
The steps performed include:
- Create a Vertex AI `Dataset` resource from `BigQuery` table -- compatible for `AutoML` training.
- Extract a copy of the dataset from `BigQuery` to a CSV file in Cloud Storage -- compatible for `AutoML` or custom training.
- Select rows from a `BigQuery` dataset into a `pandas` dataframe -- compatible for custom training.
- Select rows from a `BigQuery` dataset into a `tf.data.Dataset` -- compatible for custom training `TensorFlow` models.
- Select rows from extracted CSV files into a `tf.data.Dataset` -- compatible for custom training `TensorFlow` models.
- Create a `BigQuery` dataset from CSV files.
- Extract data from `BigQuery` table into a `DMatrix` -- compatible for custom training `XGBoost` models.
Learn more about BigQuery Datasets.
Learn more about Vertex AI for BigQuery users.
Get started with Vertex AI Data Labeling
Learn how to use the `Vertex AI Data Labeling` service.
The steps performed include:
- Create a Specialist Pool for data labelers.
- Create a data labeling job.
- Submit the data labeling job.
- List data labeling jobs.
- Cancel a data labeling job.
Learn more about Vertex AI Data Labeling.